1. Field of the Invention
The present invention relates to a method and an apparatus for suppressing noise included in an image signal representing a radiographic image. The present invention also relates to a method for suppressing noise included in an image signal representing a radiographic image. The present invention further relates to a computer-readable storage medium storing a program which instructs a computer to execute a method for suppressing noise included in an image signal representing a radiographic image.
2. Description of the Related Art
Currently, when radiographic images obtained by computed radiography (CR) or the like are used in diagnosis, image processing such as frequency emphasis processing or gradation processing is performed on the radiographic images before the radiographic images are displayed on CRT monitors as soft copies or recorded in films as hard copies.
Generally, radiographic images tend to include undesirably noticeable quantization noise in low density areas corresponding to low-intensity radiation exposure. Therefore, various methods have been proposed for suppressing noise components included in image signals carrying radiographic images.
For example, U.S. Pat. No. 5,461,655 (corresponding to Japanese Unexamined Patent Publications No. 6(1994)-96200) discloses a method for suppressing noise components in an image. In the method disclosed in U.S. Pat. No. 5,461,655, an image is transformed (decomposed) into a set of detail images (represented by band-limited image signals corresponding to 1 to M resolution levels). Then, a moving average of squared pixel values of each detail image in an N×N neighborhood centered around each pixel of interest (i.e., a sum of the squared pixel values divided by N2) is calculated as a local variance, and a histogram of the local variance is produced for each detail image. Next, a local variance corresponding to the peak in the histogram is obtained as a noise variance, and the local variance corresponding to each pixel is compared with the noise variance. When the local variance is comparable to or smaller than the noise variance, a portion of the band-limited image signal corresponding to the pixel is reduced. Thereafter, the set of detail images processed as above are inversely transformed (composed) to the space of the original image by inverse multiresolution transformation so that an image in which the noise is suppressed is obtained.
U.S. Pat. No. 5,461,655 also discloses that noise variances for detail images at lower resolution levels can be calculated based on a noise variance for a detail image at the highest resolution level, i.e., the finest-grained detail image.
According to the method disclosed in U.S. Pat. No. 5,461,655, noise is suppressed by using a noise variance obtained from local variances of each detail image and a histogram of the local variances, based on the assumption that the noise is uniformly distributed in the entire image. However, noise in actual radiographic images is not uniformly distributed. For example, noise levels are relatively high in areas in which images of objects exist, and noise levels are relatively low in areas in which images of objects do not exist. Therefore, when the method disclosed in U.S. Pat. No. 5,461,655 is applied to the actual radiographic images, edge information in the areas in which the noise levels are low is suppressed as well as the noise. That is, edge degradation occurs, and sharpness of the radiographic images decreases. Consequently, in practice, it is not possible to sufficiently suppress the noise in the areas in which the noise levels are high, by the method to disclosed in U.S. Pat. No. 5,461,655, since noise is suppressed by using the threshold value obtained from the histogram according to the method.
In addition, shapes of histograms obtained from radiographic images including images of objects having complex structures are different from shapes of histograms obtained from radiographic images including images of objects having simple structures, even when the average noise levels in the images of objects having the complex structures and those having the simple structures are almost identical. Therefore, it is difficult to desirably discriminate between edges and noise according to variations between objects.